Overview

Dataset statistics

Number of variables16
Number of observations909604
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.0 MiB
Average record size in memory128.0 B

Variable types

Categorical2
Numeric14

Alerts

timestamp has a high cardinality: 414692 distinct values High cardinality
active_power_calculated_by_converter is highly correlated with active_power_raw and 7 other fieldsHigh correlation
active_power_raw is highly correlated with active_power_calculated_by_converter and 7 other fieldsHigh correlation
generator_speed is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
generator_winding_temp_max is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
grid_power10min_average is highly correlated with active_power_calculated_by_converter and 7 other fieldsHigh correlation
nacelle_temp is highly correlated with TargetHigh correlation
reactice_power_calculated_by_converter is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
reactive_power is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
wind_speed_raw is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
wind_speed_turbulence is highly correlated with active_power_calculated_by_converter and 2 other fieldsHigh correlation
Target is highly correlated with nacelle_tempHigh correlation
active_power_calculated_by_converter is highly correlated with active_power_raw and 7 other fieldsHigh correlation
active_power_raw is highly correlated with active_power_calculated_by_converter and 7 other fieldsHigh correlation
generator_speed is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
generator_winding_temp_max is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
grid_power10min_average is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
nc1_inside_temp is highly correlated with nacelle_tempHigh correlation
nacelle_temp is highly correlated with nc1_inside_temp and 1 other fieldsHigh correlation
reactice_power_calculated_by_converter is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
reactive_power is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
wind_speed_raw is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
wind_speed_turbulence is highly correlated with active_power_calculated_by_converter and 1 other fieldsHigh correlation
Target is highly correlated with nacelle_tempHigh correlation
active_power_calculated_by_converter is highly correlated with active_power_raw and 5 other fieldsHigh correlation
active_power_raw is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
generator_speed is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
grid_power10min_average is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
reactice_power_calculated_by_converter is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
reactive_power is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
wind_speed_raw is highly correlated with active_power_calculated_by_converter and 5 other fieldsHigh correlation
active_power_calculated_by_converter is highly correlated with active_power_raw and 6 other fieldsHigh correlation
active_power_raw is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
ambient_temperature is highly correlated with nc1_inside_temp and 2 other fieldsHigh correlation
generator_speed is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
generator_winding_temp_max is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
grid_power10min_average is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
nc1_inside_temp is highly correlated with ambient_temperature and 2 other fieldsHigh correlation
nacelle_temp is highly correlated with ambient_temperature and 2 other fieldsHigh correlation
reactice_power_calculated_by_converter is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
reactive_power is highly correlated with active_power_calculated_by_converter and 6 other fieldsHigh correlation
wind_direction_raw is highly correlated with turbine_idHigh correlation
wind_speed_raw is highly correlated with active_power_calculated_by_converter and 7 other fieldsHigh correlation
wind_speed_turbulence is highly correlated with wind_speed_rawHigh correlation
turbine_id is highly correlated with ambient_temperature and 2 other fieldsHigh correlation
Target is highly correlated with nacelle_tempHigh correlation
timestamp is uniformly distributed Uniform

Reproduction

Analysis started2022-08-22 06:06:15.846808
Analysis finished2022-08-22 06:10:15.606558
Duration3 minutes and 59.76 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct414692
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2021-07-16 01:52:00
 
11
2021-08-26 10:22:00
 
11
2021-06-03 19:57:00
 
11
2021-04-02 21:49:00
 
11
2021-08-24 16:10:00
 
10
Other values (414687)
909550 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17282476
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140047 ?
Unique (%)15.4%

Sample

1st row2021-02-19 20:18:00
2nd row2021-04-27 04:55:00
3rd row2021-01-25 06:26:00
4th row2021-10-30 03:47:00
5th row2021-03-15 00:39:00

Common Values

ValueCountFrequency (%)
2021-07-16 01:52:0011
 
< 0.1%
2021-08-26 10:22:0011
 
< 0.1%
2021-06-03 19:57:0011
 
< 0.1%
2021-04-02 21:49:0011
 
< 0.1%
2021-08-24 16:10:0010
 
< 0.1%
2021-03-12 11:18:0010
 
< 0.1%
2021-06-25 02:59:0010
 
< 0.1%
2021-09-08 20:58:0010
 
< 0.1%
2021-09-08 20:53:0010
 
< 0.1%
2021-09-29 01:04:0010
 
< 0.1%
Other values (414682)909500
> 99.9%

Length

2022-08-22T11:40:17.264494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-12-312941
 
0.2%
2021-12-172928
 
0.2%
2021-12-202910
 
0.2%
2021-12-292909
 
0.2%
2021-07-222907
 
0.2%
2021-07-242903
 
0.2%
2021-04-052901
 
0.2%
2021-11-222901
 
0.2%
2021-06-072900
 
0.2%
2021-07-262896
 
0.2%
Other values (1794)1790112
98.4%

Most occurring characters

ValueCountFrequency (%)
04604875
26.6%
22849055
16.5%
12400066
13.9%
-1819208
 
10.5%
:1819208
 
10.5%
909604
 
5.3%
3567190
 
3.3%
5484736
 
2.8%
4478033
 
2.8%
7344237
 
2.0%
Other values (3)1006264
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12734456
73.7%
Dash Punctuation1819208
 
10.5%
Other Punctuation1819208
 
10.5%
Space Separator909604
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04604875
36.2%
22849055
22.4%
12400066
18.8%
3567190
 
4.5%
5484736
 
3.8%
4478033
 
3.8%
7344237
 
2.7%
8341119
 
2.7%
6339511
 
2.7%
9325634
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-1819208
100.0%
Other Punctuation
ValueCountFrequency (%)
:1819208
100.0%
Space Separator
ValueCountFrequency (%)
909604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common17282476
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04604875
26.6%
22849055
16.5%
12400066
13.9%
-1819208
 
10.5%
:1819208
 
10.5%
909604
 
5.3%
3567190
 
3.3%
5484736
 
2.8%
4478033
 
2.8%
7344237
 
2.0%
Other values (3)1006264
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII17282476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04604875
26.6%
22849055
16.5%
12400066
13.9%
-1819208
 
10.5%
:1819208
 
10.5%
909604
 
5.3%
3567190
 
3.3%
5484736
 
2.8%
4478033
 
2.8%
7344237
 
2.0%
Other values (3)1006264
 
5.8%

active_power_calculated_by_converter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct908574
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.8860615
Minimum0
Maximum1999.999858
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:17.424554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.30054725
Q1149.6208982
median379.9899292
Q3781.3574753
95-th percentile1519.630602
Maximum1999.999858
Range1999.999858
Interquartile range (IQR)631.736577

Descriptive statistics

Standard deviation474.6195757
Coefficient of variation (CV)0.9025140815
Kurtosis0.4610637422
Mean525.8860615
Median Absolute Deviation (MAD)278.2603669
Skewness1.094893808
Sum478348065.1
Variance225263.7417
MonotonicityNot monotonic
2022-08-22T11:40:17.568554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
372.39477033
 
< 0.1%
382.19807943
 
< 0.1%
807.58669032
 
< 0.1%
340.72344462
 
< 0.1%
318.35483812
 
< 0.1%
317.46837872
 
< 0.1%
1002.1832282
 
< 0.1%
482.04934692
 
< 0.1%
217.08933772
 
< 0.1%
262.71811932
 
< 0.1%
Other values (908564)909582
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
1.42424793 × 10-51
< 0.1%
2.624800363 × 10-51
< 0.1%
3.169228633 × 10-51
< 0.1%
4.995862643 × 10-51
< 0.1%
5.025905229 × 10-51
< 0.1%
6.226457662 × 10-51
< 0.1%
0.00010357176261
< 0.1%
0.00011028667391
< 0.1%
0.00025909487161
< 0.1%
ValueCountFrequency (%)
1999.9998581
< 0.1%
1999.9989221
< 0.1%
1999.9981281
< 0.1%
1999.9947511
< 0.1%
1999.9938151
< 0.1%
1999.9902141
< 0.1%
1999.9756271
< 0.1%
1999.9666341
< 0.1%
1999.9570071
< 0.1%
1999.9546241
< 0.1%

active_power_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct908591
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.6394682
Minimum8.443594197 × 10-6
Maximum1999.984456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:17.736520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.443594197 × 10-6
5-th percentile25.9436022
Q1155.8132439
median383.9859263
Q3800.2611084
95-th percentile1557.439565
Maximum1999.984456
Range1999.984448
Interquartile range (IQR)644.4478645

Descriptive statistics

Standard deviation482.3949074
Coefficient of variation (CV)0.897246084
Kurtosis0.356481929
Mean537.6394682
Median Absolute Deviation (MAD)276.578481
Skewness1.079559874
Sum489039010.8
Variance232704.8467
MonotonicityNot monotonic
2022-08-22T11:40:17.886571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
855.36967983
 
< 0.1%
365.56324263
 
< 0.1%
306.05120852
 
< 0.1%
652.54061892
 
< 0.1%
208.57553862
 
< 0.1%
216.30260472
 
< 0.1%
674.6626182
 
< 0.1%
1153.8722132
 
< 0.1%
342.62875872
 
< 0.1%
561.56468712
 
< 0.1%
Other values (908581)909582
> 99.9%
ValueCountFrequency (%)
8.443594197 × 10-61
< 0.1%
0.00013085603131
< 0.1%
0.00014396668121
< 0.1%
0.00043662340611
< 0.1%
0.00043787850881
< 0.1%
0.00046843409651
< 0.1%
0.00056021718771
< 0.1%
0.00062810035891
< 0.1%
0.00068150280281
< 0.1%
0.0007186103031
< 0.1%
ValueCountFrequency (%)
1999.9844561
< 0.1%
1999.951091
< 0.1%
1999.9404911
< 0.1%
1999.938661
< 0.1%
1999.9146121
< 0.1%
1999.8699541
< 0.1%
1999.8337611
< 0.1%
1999.7667441
< 0.1%
1999.7406621
< 0.1%
1999.7242431
< 0.1%

ambient_temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct902272
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.57392143
Minimum5.616541386
Maximum48.08901672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:18.048364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.616541386
5-th percentile12.24757375
Q117.09367959
median27.91903458
Q332.17169571
95-th percentile35.52602758
Maximum48.08901672
Range42.47247534
Interquartile range (IQR)15.07801612

Descriptive statistics

Standard deviation8.025649303
Coefficient of variation (CV)0.3138216142
Kurtosis-1.102809748
Mean25.57392143
Median Absolute Deviation (MAD)5.354522864
Skewness-0.4056062668
Sum23262141.22
Variance64.41104673
MonotonicityNot monotonic
2022-08-22T11:40:18.192366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.064979554
 
< 0.1%
34.021835334
 
< 0.1%
31.607709883
 
< 0.1%
32.083324433
 
< 0.1%
33.716552733
 
< 0.1%
32.02367023
 
< 0.1%
33.126873023
 
< 0.1%
26.42096713
 
< 0.1%
33.709331513
 
< 0.1%
12.60029033
 
< 0.1%
Other values (902262)909572
> 99.9%
ValueCountFrequency (%)
5.6165413861
< 0.1%
5.6365728381
< 0.1%
5.6571588521
< 0.1%
5.675493361
< 0.1%
5.6786012651
< 0.1%
5.6872444151
< 0.1%
5.690174581
< 0.1%
5.6933539711
< 0.1%
5.7026946541
< 0.1%
5.7157540321
< 0.1%
ValueCountFrequency (%)
48.089016721
< 0.1%
47.950532791
< 0.1%
47.94084931
< 0.1%
47.907621381
< 0.1%
47.878091811
< 0.1%
47.823004721
< 0.1%
47.821601871
< 0.1%
47.802864071
< 0.1%
47.783761021
< 0.1%
47.6970521
< 0.1%

generator_speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102172
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean931.1308817
Minimum0
Maximum1267.140625
Zeros100
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:18.343779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile761.0166931
Q1770.5385742
median884.0685018
Q31123.335856
95-th percentile1200.243164
Maximum1267.140625
Range1267.140625
Interquartile range (IQR)352.7972819

Descriptive statistics

Standard deviation193.6743836
Coefficient of variation (CV)0.2079990981
Kurtosis1.561075179
Mean931.1308817
Median Absolute Deviation (MAD)114.4377035
Skewness-0.4328699721
Sum846960374.5
Variance37509.76685
MonotonicityNot monotonic
2022-08-22T11:40:18.487778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
769.806121836916
 
4.1%
770.538574224665
 
2.7%
769.073669414186
 
1.6%
770.050272614114
 
1.6%
769.56197112371
 
1.4%
769.95261239257
 
1.0%
770.24559338679
 
1.0%
769.65963138110
 
0.9%
770.1723487482
 
0.8%
769.36665045087
 
0.6%
Other values (102162)768737
84.5%
ValueCountFrequency (%)
0100
< 0.1%
2.384185791 × 10-81
 
< 0.1%
8.514949254 × 10-81
 
< 0.1%
9.934107463 × 10-82
 
< 0.1%
1.192092896 × 10-74
 
< 0.1%
1.682954676 × 10-71
 
< 0.1%
1.788139343 × 10-71
 
< 0.1%
2.58286794 × 10-71
 
< 0.1%
3.973642985 × 10-72
 
< 0.1%
0.001456165291
 
< 0.1%
ValueCountFrequency (%)
1267.1406251
< 0.1%
1241.1034891
< 0.1%
1237.1099851
< 0.1%
1232.4244431
< 0.1%
1230.0281521
< 0.1%
1227.6675421
< 0.1%
1225.3069311
< 0.1%
1223.7453981
< 0.1%
1222.946321
< 0.1%
1220.5857091
< 0.1%

generator_winding_temp_max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct902324
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.73011204
Minimum25.63636557
Maximum129.846405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:18.647777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25.63636557
5-th percentile45.65135899
Q154.91676693
median60.03448963
Q365.73718719
95-th percentile78.44303102
Maximum129.846405
Range104.2100395
Interquartile range (IQR)10.82042027

Descriptive statistics

Standard deviation9.72158221
Coefficient of variation (CV)0.1600784501
Kurtosis1.217988071
Mean60.73011204
Median Absolute Deviation (MAD)5.382360649
Skewness0.482790834
Sum55240352.83
Variance94.50916066
MonotonicityNot monotonic
2022-08-22T11:40:18.771079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.407320023
 
< 0.1%
54.572755183
 
< 0.1%
72.417003633
 
< 0.1%
72.91522983
 
< 0.1%
64.012420653
 
< 0.1%
60.864402143
 
< 0.1%
69.586112983
 
< 0.1%
57.782906853
 
< 0.1%
68.443527223
 
< 0.1%
55.469625473
 
< 0.1%
Other values (902314)909574
> 99.9%
ValueCountFrequency (%)
25.636365571
< 0.1%
25.842157361
< 0.1%
25.92892521
< 0.1%
25.970747631
< 0.1%
26.037995021
< 0.1%
26.194313431
< 0.1%
26.204429311
< 0.1%
26.234103841
< 0.1%
26.262740771
< 0.1%
26.277231851
< 0.1%
ValueCountFrequency (%)
129.8464051
< 0.1%
129.79853311
< 0.1%
128.9377351
< 0.1%
128.75165711
< 0.1%
127.77829741
< 0.1%
127.5743241
< 0.1%
124.7132391
< 0.1%
121.92652381
< 0.1%
120.25229641
< 0.1%
117.71233371
< 0.1%

grid_power10min_average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct908371
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean527.5795372
Minimum0
Maximum1999.991455
Zeros23
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:18.911696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.998432296
Q1147.1815516
median391.3031871
Q3794.8757095
95-th percentile1507.971682
Maximum1999.991455
Range1999.991455
Interquartile range (IQR)647.6941579

Descriptive statistics

Standard deviation472.6569764
Coefficient of variation (CV)0.8958970982
Kurtosis0.2352332911
Mean527.5795372
Median Absolute Deviation (MAD)291.3128173
Skewness1.00806151
Sum479888457.3
Variance223404.6174
MonotonicityNot monotonic
2022-08-22T11:40:19.170175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023
 
< 0.1%
299.81395472
 
< 0.1%
603.10012822
 
< 0.1%
100.03041712
 
< 0.1%
948.81347662
 
< 0.1%
530.81653852
 
< 0.1%
466.27816772
 
< 0.1%
50.042008722
 
< 0.1%
636.43304442
 
< 0.1%
489.14427692
 
< 0.1%
Other values (908361)909563
> 99.9%
ValueCountFrequency (%)
023
< 0.1%
0.00041850407921
 
< 0.1%
0.00045043183491
 
< 0.1%
0.00048684949681
 
< 0.1%
0.00060194730761
 
< 0.1%
0.0011188299371
 
< 0.1%
0.0012785159051
 
< 0.1%
0.0012915382781
 
< 0.1%
0.0013741130631
 
< 0.1%
0.0013882964851
 
< 0.1%
ValueCountFrequency (%)
1999.9914551
< 0.1%
1999.9853111
< 0.1%
1999.9435421
< 0.1%
1999.9412841
< 0.1%
1999.9409381
< 0.1%
1999.9390671
< 0.1%
1999.9368081
< 0.1%
1999.9363811
< 0.1%
1999.9361781
< 0.1%
1999.9310511
< 0.1%

nc1_inside_temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct889309
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.75582325
Minimum11.14192224
Maximum52.79003016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:19.310792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11.14192224
5-th percentile26.07736015
Q129.91380334
median31.76294597
Q333.46974513
95-th percentile38.55420765
Maximum52.79003016
Range41.64810793
Interquartile range (IQR)3.555941788

Descriptive statistics

Standard deviation3.929794339
Coefficient of variation (CV)0.1237503531
Kurtosis4.299023904
Mean31.75582325
Median Absolute Deviation (MAD)1.768719832
Skewness0.04052958733
Sum28885223.85
Variance15.44328355
MonotonicityNot monotonic
2022-08-22T11:40:19.451424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.722415925
 
< 0.1%
33.523025515
 
< 0.1%
33.416988375
 
< 0.1%
33.121440895
 
< 0.1%
32.822532655
 
< 0.1%
33.921325685
 
< 0.1%
30.616939544
 
< 0.1%
32.919837954
 
< 0.1%
32.232364654
 
< 0.1%
33.259910584
 
< 0.1%
Other values (889299)909558
> 99.9%
ValueCountFrequency (%)
11.141922241
< 0.1%
11.149836221
< 0.1%
11.19155551
< 0.1%
11.208829161
< 0.1%
11.211997991
< 0.1%
11.213145261
< 0.1%
11.214869821
< 0.1%
11.257230121
< 0.1%
11.260712151
< 0.1%
11.281429771
< 0.1%
ValueCountFrequency (%)
52.790030161
< 0.1%
52.546735131
< 0.1%
52.53594361
< 0.1%
52.514169851
< 0.1%
52.514064791
< 0.1%
52.503807071
< 0.1%
52.481604581
< 0.1%
52.428150181
< 0.1%
52.385896051
< 0.1%
52.303871151
< 0.1%

nacelle_temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct886877
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.68641306
Minimum16.01021767
Maximum48.40610631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:19.576425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.01021767
5-th percentile24.85664759
Q130.26742258
median31.96161008
Q333.54625975
95-th percentile36.98366802
Maximum48.40610631
Range32.39588865
Interquartile range (IQR)3.278837172

Descriptive statistics

Standard deviation3.55442601
Coefficient of variation (CV)0.1121750828
Kurtosis1.922251785
Mean31.68641306
Median Absolute Deviation (MAD)1.635901848
Skewness-0.4275419016
Sum28822088.07
Variance12.63394426
MonotonicityNot monotonic
2022-08-22T11:40:19.717050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.821941386
 
< 0.1%
32.221763615
 
< 0.1%
32.221858985
 
< 0.1%
31.123304375
 
< 0.1%
32.828289035
 
< 0.1%
29.524271015
 
< 0.1%
32.222385415
 
< 0.1%
32.326896675
 
< 0.1%
33.221546175
 
< 0.1%
32.521812445
 
< 0.1%
Other values (886867)909553
> 99.9%
ValueCountFrequency (%)
16.010217671
< 0.1%
16.021014051
< 0.1%
16.302117031
< 0.1%
16.309542661
< 0.1%
16.383406961
< 0.1%
16.612108231
< 0.1%
16.641022051
< 0.1%
16.656161311
< 0.1%
16.693165591
< 0.1%
16.703798291
< 0.1%
ValueCountFrequency (%)
48.406106311
< 0.1%
48.3919151
< 0.1%
48.377723691
< 0.1%
48.319957731
< 0.1%
48.262110141
< 0.1%
48.234870911
< 0.1%
48.230304721
< 0.1%
48.225738531
< 0.1%
48.223451611
< 0.1%
48.209825521
< 0.1%

reactice_power_calculated_by_converter
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct905428
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.0806104
Minimum-318.3937215
Maximum523.8926951
Zeros2
Zeros (%)< 0.1%
Negative13954
Negative (%)1.5%
Memory size6.9 MiB
2022-08-22T11:40:19.857668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-318.3937215
5-th percentile2.048212675
Q181.54084953
median92.00582631
Q3124.8791529
95-th percentile278.1797583
Maximum523.8926951
Range842.2864166
Interquartile range (IQR)43.33830341

Descriptive statistics

Standard deviation78.21994359
Coefficient of variation (CV)0.7041727923
Kurtosis4.080529096
Mean111.0806104
Median Absolute Deviation (MAD)19.8386062
Skewness1.731298225
Sum101039367.5
Variance6118.359575
MonotonicityNot monotonic
2022-08-22T11:40:19.997876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.392303473
 
< 0.1%
86.84082543
 
< 0.1%
91.345376333
 
< 0.1%
84.802680973
 
< 0.1%
90.399066933
 
< 0.1%
100.63079833
 
< 0.1%
93.832185113
 
< 0.1%
93.175490063
 
< 0.1%
116.68737283
 
< 0.1%
86.094534563
 
< 0.1%
Other values (905418)909574
> 99.9%
ValueCountFrequency (%)
-318.39372151
< 0.1%
-302.09777151
< 0.1%
-293.94979651
< 0.1%
-164.94618761
< 0.1%
-159.94297891
< 0.1%
-134.92693571
< 0.1%
-129.9237271
< 0.1%
-108.52037471
< 0.1%
-70.552699531
< 0.1%
-63.610610281
< 0.1%
ValueCountFrequency (%)
523.89269511
< 0.1%
522.69706731
< 0.1%
520.72623191
< 0.1%
519.56777951
< 0.1%
511.52762351
< 0.1%
510.6903331
< 0.1%
510.30430091
< 0.1%
508.61284891
< 0.1%
508.60071311
< 0.1%
508.36238611
< 0.1%

reactive_power
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct903958
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.6923334
Minimum-5.46160717
Maximum592.8159383
Zeros5
Zeros (%)< 0.1%
Negative11849
Negative (%)1.3%
Memory size6.9 MiB
2022-08-22T11:40:20.137390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-5.46160717
5-th percentile1.722649301
Q196.04576461
median107.6333777
Q3154.1537304
95-th percentile329.3071905
Maximum592.8159383
Range598.2775455
Interquartile range (IQR)58.10796579

Descriptive statistics

Standard deviation91.23215183
Coefficient of variation (CV)0.6824037665
Kurtosis3.537841443
Mean133.6923334
Median Absolute Deviation (MAD)28.14022832
Skewness1.638331752
Sum121607081.2
Variance8323.305527
MonotonicityNot monotonic
2022-08-22T11:40:20.262390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.605193857 × 10-45121
 
< 0.1%
-5.605193857 × 10-457
 
< 0.1%
05
 
< 0.1%
95.407749184
 
< 0.1%
95.072982794
 
< 0.1%
100.1352453
 
< 0.1%
97.440605163
 
< 0.1%
97.294648493
 
< 0.1%
102.13592153
 
< 0.1%
155.79475153
 
< 0.1%
Other values (903948)909448
> 99.9%
ValueCountFrequency (%)
-5.461607171
< 0.1%
-5.3050590751
< 0.1%
-5.1283117141
< 0.1%
-5.0733881791
< 0.1%
-4.8931441311
< 0.1%
-4.8766227721
< 0.1%
-4.8498612241
< 0.1%
-4.7574041681
< 0.1%
-4.7531987191
< 0.1%
-4.7462127211
< 0.1%
ValueCountFrequency (%)
592.81593831
< 0.1%
589.86258951
< 0.1%
587.57017011
< 0.1%
586.43199671
< 0.1%
585.23310341
< 0.1%
584.91478471
< 0.1%
582.51678471
< 0.1%
582.43878171
< 0.1%
582.03535971
< 0.1%
581.90847781
< 0.1%

wind_direction_raw
Real number (ℝ≥0)

HIGH CORRELATION

Distinct905356
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.4871609
Minimum0.3096923828
Maximum359.4555308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:20.403007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.3096923828
5-th percentile24.53432315
Q198.91606013
median232.8211594
Q3287.6675034
95-th percentile336.6906916
Maximum359.4555308
Range359.1458384
Interquartile range (IQR)188.7514432

Descriptive statistics

Standard deviation104.6172613
Coefficient of variation (CV)0.5244310503
Kurtosis-1.26812064
Mean199.4871609
Median Absolute Deviation (MAD)79.0753212
Skewness-0.3639712865
Sum181454319.5
Variance10944.77136
MonotonicityNot monotonic
2022-08-22T11:40:20.543636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291.78848273
 
< 0.1%
282.44589233
 
< 0.1%
284.53334053
 
< 0.1%
287.96106473
 
< 0.1%
277.10574343
 
< 0.1%
277.06071473
 
< 0.1%
279.8401543
 
< 0.1%
275.96530153
 
< 0.1%
272.39462283
 
< 0.1%
304.87369793
 
< 0.1%
Other values (905346)909574
> 99.9%
ValueCountFrequency (%)
0.30969238281
< 0.1%
0.60468546551
< 0.1%
0.67307027181
< 0.1%
0.72056579591
< 0.1%
0.7928763391
< 0.1%
0.81449381511
< 0.1%
0.83546956381
< 0.1%
0.90074462891
< 0.1%
0.9064636231
< 0.1%
0.91385650631
< 0.1%
ValueCountFrequency (%)
359.45553081
< 0.1%
359.35881041
< 0.1%
359.32860311
< 0.1%
359.31634521
< 0.1%
359.18685911
< 0.1%
359.1018271
< 0.1%
359.08424891
< 0.1%
359.01371261
< 0.1%
359.0071921
< 0.1%
359.00472011
< 0.1%

wind_speed_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct823978
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.894949748
Minimum0.9044999828
Maximum19.79500071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:20.684263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9044999828
5-th percentile3.229408822
Q14.400753091
median5.637206594
Q37.092771312
95-th percentile9.567495966
Maximum19.79500071
Range18.89050073
Interquartile range (IQR)2.692018221

Descriptive statistics

Standard deviation1.97768399
Coefficient of variation (CV)0.3354878454
Kurtosis0.6414064659
Mean5.894949748
Median Absolute Deviation (MAD)1.328584154
Skewness0.7183274163
Sum5362069.87
Variance3.911233963
MonotonicityNot monotonic
2022-08-22T11:40:20.824895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.57620000833
 
< 0.1%
5.55142855632
 
< 0.1%
5.61554288931
 
< 0.1%
5.53102858931
 
< 0.1%
5.59077143729
 
< 0.1%
5.4800285529
 
< 0.1%
5.40134286927
 
< 0.1%
5.57328573927
 
< 0.1%
5.42757145626
 
< 0.1%
5.51354289125
 
< 0.1%
Other values (823968)909314
> 99.9%
ValueCountFrequency (%)
0.90449998281
< 0.1%
0.90450000761
< 0.1%
0.90822223821
< 0.1%
0.91380555431
< 0.1%
0.91566665971
< 0.1%
0.9175277751
< 0.1%
0.92013334041
< 0.1%
0.92124998571
< 0.1%
0.92218054331
< 0.1%
0.92236666681
< 0.1%
ValueCountFrequency (%)
19.795000711
< 0.1%
19.789999961
< 0.1%
19.789167091
< 0.1%
19.784166971
< 0.1%
19.777167971
< 0.1%
19.771667161
< 0.1%
19.771666841
< 0.1%
19.735037481
< 0.1%
19.650776511
< 0.1%
19.62333331
< 0.1%

wind_speed_turbulence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct906984
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6519156608
Minimum0
Maximum9.534769058
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:20.981132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28687598
Q10.4321290081
median0.5838627726
Q30.798239775
95-th percentile1.232609609
Maximum9.534769058
Range9.534769058
Interquartile range (IQR)0.3661107669

Descriptive statistics

Standard deviation0.3175132374
Coefficient of variation (CV)0.4870464947
Kurtosis15.8708017
Mean0.6519156608
Median Absolute Deviation (MAD)0.1738885467
Skewness2.20547233
Sum592985.0927
Variance0.1008146559
MonotonicityNot monotonic
2022-08-22T11:40:21.122251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
< 0.1%
0.78602617983
 
< 0.1%
0.29123517873
 
< 0.1%
0.47772401573
 
< 0.1%
0.56406641013
 
< 0.1%
0.61931818723
 
< 0.1%
0.74340599783
 
< 0.1%
0.52818969893
 
< 0.1%
0.71735525133
 
< 0.1%
0.74303787953
 
< 0.1%
Other values (906974)909555
> 99.9%
ValueCountFrequency (%)
022
< 0.1%
7.770637877 × 10-51
 
< 0.1%
0.00015541275751
 
< 0.1%
0.0003641141081
 
< 0.1%
0.00046623827261
 
< 0.1%
0.00079239717871
 
< 0.1%
0.0012062992971
 
< 0.1%
0.0022514662421
 
< 0.1%
0.002477789041
 
< 0.1%
0.0036188978911
 
< 0.1%
ValueCountFrequency (%)
9.5347690581
< 0.1%
9.0414506591
< 0.1%
8.7752063751
< 0.1%
8.6427199051
< 0.1%
8.4285551711
< 0.1%
8.2476226491
< 0.1%
7.9147591591
< 0.1%
7.5140182181
< 0.1%
7.2605706851
< 0.1%
7.1540258411
< 0.1%

turbine_id
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Turbine_15
 
58048
Turbine_18
 
57892
Turbine_120
 
57754
Turbine_97
 
57683
Turbine_158
 
57470
Other values (11)
620757 

Length

Max length11
Median length10
Mean length10.43982326
Min length10

Characters and Unicode

Total characters9496105
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurbine_108
2nd rowTurbine_18
3rd rowTurbine_105
4th rowTurbine_15
5th rowTurbine_01

Common Values

ValueCountFrequency (%)
Turbine_1558048
 
6.4%
Turbine_1857892
 
6.4%
Turbine_12057754
 
6.3%
Turbine_9757683
 
6.3%
Turbine_15857470
 
6.3%
Turbine_10857401
 
6.3%
Turbine_10557341
 
6.3%
Turbine_10356944
 
6.3%
Turbine_1456934
 
6.3%
Turbine_13956930
 
6.3%
Other values (6)335207
36.9%

Length

2022-08-22T11:40:21.333527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
turbine_1558048
 
6.4%
turbine_1857892
 
6.4%
turbine_12057754
 
6.3%
turbine_9757683
 
6.3%
turbine_15857470
 
6.3%
turbine_10857401
 
6.3%
turbine_10557341
 
6.3%
turbine_10356944
 
6.3%
turbine_1456934
 
6.3%
turbine_13956930
 
6.3%
Other values (6)335207
36.9%

Most occurring characters

ValueCountFrequency (%)
T909604
9.6%
u909604
9.6%
r909604
9.6%
b909604
9.6%
i909604
9.6%
n909604
9.6%
e909604
9.6%
_909604
9.6%
1795398
8.4%
0395223
 
4.2%
Other values (7)1028652
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5457624
57.5%
Decimal Number2219273
23.4%
Uppercase Letter909604
 
9.6%
Connector Punctuation909604
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1795398
35.8%
0395223
17.8%
3226952
 
10.2%
5172859
 
7.8%
8172763
 
7.8%
9170959
 
7.7%
2170502
 
7.7%
757683
 
2.6%
456934
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
u909604
16.7%
r909604
16.7%
b909604
16.7%
i909604
16.7%
n909604
16.7%
e909604
16.7%
Uppercase Letter
ValueCountFrequency (%)
T909604
100.0%
Connector Punctuation
ValueCountFrequency (%)
_909604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6367228
67.1%
Common3128877
32.9%

Most frequent character per script

Common
ValueCountFrequency (%)
_909604
29.1%
1795398
25.4%
0395223
12.6%
3226952
 
7.3%
5172859
 
5.5%
8172763
 
5.5%
9170959
 
5.5%
2170502
 
5.4%
757683
 
1.8%
456934
 
1.8%
Latin
ValueCountFrequency (%)
T909604
14.3%
u909604
14.3%
r909604
14.3%
b909604
14.3%
i909604
14.3%
n909604
14.3%
e909604
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9496105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T909604
9.6%
u909604
9.6%
r909604
9.6%
b909604
9.6%
i909604
9.6%
n909604
9.6%
e909604
9.6%
_909604
9.6%
1795398
8.4%
0395223
 
4.2%
Other values (7)1028652
10.8%

Target
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861262
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3285948
Minimum25.86532021
Maximum65.03768921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2022-08-22T11:40:21.458523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25.86532021
5-th percentile42.26528721
Q144.81674027
median46.3028986
Q347.73310494
95-th percentile50.04729681
Maximum65.03768921
Range39.172369
Interquartile range (IQR)2.91636467

Descriptive statistics

Standard deviation2.617691223
Coefficient of variation (CV)0.05650271143
Kurtosis3.915566828
Mean46.3285948
Median Absolute Deviation (MAD)1.464714766
Skewness0.7053594019
Sum42140675.14
Variance6.852307337
MonotonicityNot monotonic
2022-08-22T11:40:21.583523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.521888736
 
< 0.1%
45.714832315
 
< 0.1%
44.196239475
 
< 0.1%
45.612213135
 
< 0.1%
46.330680855
 
< 0.1%
45.311557775
 
< 0.1%
48.021759035
 
< 0.1%
48.424766545
 
< 0.1%
45.921943665
 
< 0.1%
47.016544345
 
< 0.1%
Other values (861252)909553
> 99.9%
ValueCountFrequency (%)
25.865320211
< 0.1%
25.954773591
< 0.1%
26.025819121
< 0.1%
26.284803391
< 0.1%
26.507840161
< 0.1%
26.527096271
< 0.1%
26.70427641
< 0.1%
26.783089641
< 0.1%
26.790182751
< 0.1%
26.804368971
< 0.1%
ValueCountFrequency (%)
65.037689211
< 0.1%
65.02556611
< 0.1%
65.023040771
< 0.1%
65.018321231
< 0.1%
65.01733781
< 0.1%
65.017102811
< 0.1%
65.014640811
< 0.1%
65.004165651
< 0.1%
65.003616331
< 0.1%
64.979461671
< 0.1%

Interactions

2022-08-22T11:40:01.744228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:28.742354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:36.217936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:43.455253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:50.271775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:57.301522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:04.296316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:11.590518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:18.596878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:25.655852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:32.662010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:39.500869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:46.463213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:54.009945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:02.274473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:29.429924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:36.773633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:43.951836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:50.760292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:57.822924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:04.841522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:12.109288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:19.149982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:26.155823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:33.179283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:40.014498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:47.001111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:54.513950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:02.833246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:29.966202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:37.260719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:44.433669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:51.248265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:58.326534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:05.448415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:12.600066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:19.666197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:26.715670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:33.674722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:40.512187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:47.529978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:55.177915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:03.356681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:30.466058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:37.794811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:44.913438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:51.698632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:58.854011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:05.931734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:13.121047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:20.145102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:27.161136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:34.160272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:40.956070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:48.077612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:55.953688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:03.914378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:31.001834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:38.282775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:45.385403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:52.191063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:59.316475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:06.426241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:13.595290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:20.637484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:27.661361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:34.664954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:41.446558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:48.621612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:56.455815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:04.447553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:31.506082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:38.788593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:45.881434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:52.799629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:59.815163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:06.957076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:14.140870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:21.144155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:28.111707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:35.148826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:41.957362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:49.105448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:56.953321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:04.980800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:32.085892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:39.289136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:46.385118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:53.294328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:00.350389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:07.485112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:14.605172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:21.658045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:28.578885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:35.612551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:42.458743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:49.655285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:57.486154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:05.517757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:32.597850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:39.793146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:46.881119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:53.759742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:00.821736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:08.013127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:15.068122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:22.136385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:29.053774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:36.117459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:43.059426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:50.199317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:57.942549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:06.029331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:33.122462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:40.430235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:47.336909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:54.247709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:01.312050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:08.556100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:15.546585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:22.628858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:29.541181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:36.572984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:43.519726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:50.673912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:58.421949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:06.577881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:33.626011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:40.932400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:47.825089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:54.727584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:01.806300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:09.096693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:16.045077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:23.162966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:30.026170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:37.054763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:44.002306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:51.137922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:58.937887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:07.225879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:34.152630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:41.452398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:48.304428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:55.206403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:02.304024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:09.596249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:16.515003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:23.645679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:30.637796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:37.537720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:44.491688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:51.687752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:59.459701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:07.825879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:34.663953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:41.930093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:48.808705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:55.735193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:02.814585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:10.097733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:16.982357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:24.147580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:31.185994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:38.040706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:45.008265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:52.231748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:00.027988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:08.393885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:35.181921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:42.434100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:49.296562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:56.301493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:03.317747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:10.605103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:17.484136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:24.654624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:31.647806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:38.527767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:45.502602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:52.921918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:00.562393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:08.969885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:35.701926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:42.937263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:49.776601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:38:56.773515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:03.805731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:11.088356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:18.106510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:25.168447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:32.204918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:39.029436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:45.978794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:39:53.473927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-22T11:40:01.193724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-22T11:40:21.724155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-22T11:40:22.005773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-22T11:40:22.293584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-22T11:40:22.548398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-22T11:40:09.471921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-22T11:40:10.596514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

timestampactive_power_calculated_by_converteractive_power_rawambient_temperaturegenerator_speedgenerator_winding_temp_maxgrid_power10min_averagenc1_inside_tempnacelle_tempreactice_power_calculated_by_converterreactive_powerwind_direction_rawwind_speed_rawwind_speed_turbulenceturbine_idTarget
02021-02-19 20:18:00816.636759834.91720631.6943801159.61660265.954214917.89708531.88197231.504713141.457644165.501518280.8647827.0570000.544082Turbine_10847.582787
12021-04-27 04:55:00419.107829421.05087312.894948928.74799659.571319445.55425032.42370532.75577089.186457113.835236299.5524605.4749370.469031Turbine_1846.070328
22021-01-25 06:26:001303.5305581337.56614216.6483881201.21977561.2704981364.71600311.44684918.332985230.622309281.45225384.9601068.0924570.622318Turbine_10539.989236
32021-10-30 03:47:0061.49487253.48100828.388141769.80612240.67434814.32489734.25320432.66288966.21101575.01753187.2611194.0710320.760719Turbine_1546.056587
42021-03-15 00:39:00593.514364611.65910831.5195271046.91676864.341763599.02017232.40558631.466387137.163938160.202421313.7248186.3579430.346068Turbine_0154.346095
52021-05-28 06:33:00603.877940620.63758328.055220955.60456360.882138697.72173131.01220731.05466857.95046775.779110229.5716656.0260290.779887Turbine_1547.947959
62021-01-02 01:00:00282.870092281.76385517.984232811.26287847.655467357.89125127.03374423.92423381.661122104.133497256.9138015.6924310.892567Turbine_1438.088093
72021-05-17 00:45:00963.812815995.34087132.1550751191.25852168.535469996.88878433.53712532.077898177.889793216.991676186.5398976.8745571.814122Turbine_0148.529146
82021-08-11 12:22:00662.245916673.99204039.1536151065.71643160.585338487.27352938.90618237.600506121.700999143.405711316.7318376.5015200.874442Turbine_1048.912348
92021-07-15 14:44:00468.044851469.82818617.189911876.03505563.952785675.51067131.46915533.11748587.047708119.317791175.5832774.8645940.996903Turbine_10347.616581

Last rows

timestampactive_power_calculated_by_converteractive_power_rawambient_temperaturegenerator_speedgenerator_winding_temp_maxgrid_power10min_averagenc1_inside_tempnacelle_tempreactice_power_calculated_by_converterreactive_powerwind_direction_rawwind_speed_rawwind_speed_turbulenceturbine_idTarget
9095942021-08-12 20:58:00367.788884369.05384834.801825875.59297463.625823502.78928634.62234533.85205792.424756110.752061342.9209855.4097710.603152Turbine_1048.889571
9095952021-08-12 16:55:00223.330709214.25454731.858798771.83084160.192288388.42547131.45338929.52080986.549025100.835799219.3800304.9109920.479672Turbine_9746.716609
9095962021-05-01 03:35:00302.359060296.22499616.927406828.43312656.357490343.33010933.67467331.71998474.125481104.579052235.7429685.3343140.319832Turbine_12345.109717
9095972021-05-25 11:49:00416.748281416.25502032.506697927.03899161.528378353.01970430.00447332.24150494.939171117.73373099.0685675.7220230.581206Turbine_10847.893845
9095982021-02-11 04:49:00102.30838592.81541422.041527769.63077844.864599150.80447633.51222630.56073386.92582498.31025124.6326814.7051680.374873Turbine_10346.223015
9095992021-04-25 19:12:00929.101908948.44190532.7360761187.30319283.1871401343.32057732.29139135.152280152.216446191.088800189.9018127.1553431.028960Turbine_1350.768675
9096002021-02-20 17:37:00100.73052688.69459930.540500770.24559356.235497177.62055230.43430229.93214185.35310796.62167593.4175904.1216070.595874Turbine_15844.234821
9096012021-10-22 14:18:001120.9159651165.01690730.9011291170.45686062.942943873.41462230.49181833.08516442.82021074.111173230.9883988.5516690.951241Turbine_1546.942486
9096022021-02-08 22:03:00123.444564116.06691932.697933770.19079657.24520795.86570432.33882431.52538486.54909497.47098465.4088014.3240640.247335Turbine_9746.392221
9096032021-04-09 14:28:004.84314710.77973419.452520736.60166460.60388578.55976730.79893238.2522002.7632311.32019811.1277392.0174440.963029Turbine_10548.902180